Feature Selection Package - Algorithms - Kruskal-Wallis

Description

Kruskal-Wallis is a test of variance using population variance among groups.
This function serves as a wrapper to the MATLAB function *kruskalwallis*.

Usage

Method Signature:

Output:

*out: * A struct containing the following fields

Input:

*X: *
The features on current trunk, each column is a feature vector on all
instances, and each row is a part of the instance.

*Y: *
The label of instances, in single column form: 1 2 3 4 5 ...

[*out*] = fsKruskalWallis(*X*, *Y*)

Output:

- W - The distribution at each data point.
- fList - The list of features that are deemed useful.
- prf - This means that the smaller the feature weight is, the more useful it will be to the user.

Input:

Code Example

% Using the wine.dat data set, which can be found at

% [fspackage_location]/classifiers/knn/wine.mat

fsKruskalWallis(X,Y)

% [fspackage_location]/classifiers/knn/wine.mat

fsKruskalWallis(X,Y)

Keyword in Evaluator Framework

kruskalwallis

Paper

BibTex entry for:

Asymptotic Conservativeness and Efficiency of Kruskal-Wallis Test for K Dependent Samples by L.J. Wei.

Asymptotic Conservativeness and Efficiency of Kruskal-Wallis Test for K Dependent Samples by L.J. Wei.

@article { wei81,

author = {Wei, L. J.},

title = {Asymptotic Conservativeness and Efficiency of Kruskal-Wallis Test for K Dependent Samples},

journal = {Journal of the American Statistical Association},

volume = {76},

number = {376},

month = {December},

pages = {1006--1009},

year = {1981}

}

author = {Wei, L. J.},

title = {Asymptotic Conservativeness and Efficiency of Kruskal-Wallis Test for K Dependent Samples},

journal = {Journal of the American Statistical Association},

volume = {76},

number = {376},

month = {December},

pages = {1006--1009},

year = {1981}

}